{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a6a4693e-86ee-4a29-9612-219d1e015886",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "success0.\n",
      "success1.\n",
      "success2.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating embeddings: 100%|█████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 8507.72it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "success3.\n"
     ]
    }
   ],
   "source": [
    "from glob import glob\n",
    "from pymilvus import model as milvus_model\n",
    "from pymilvus import MilvusClient\n",
    "from tqdm import tqdm\n",
    "\n",
    "#构造产品介绍文本\n",
    "text_lines = [\n",
    "    \"深海蓝藻保湿面膜：核心成分为深海蓝藻提取物，富含多糖和氨基酸，能深层补水、修护肌肤屏障、舒缓敏感泛红。质地清爽不粘腻，适合所有肤质，尤其适合干燥、敏感肌。规格：25ml*5片。\",\n",
    "    \"美白精华：核心成分是烟酰胺和VC衍生物，主要功效是提亮肤色、淡化痘印、改善暗沉。质地轻薄易吸收，适合需要均匀肤色的人群。\",\n",
    "    \"男士护肤精华：核心成分是烟酰胺和VC衍生物，主要功效是皮肤干爽。质地轻薄易吸收，适合需要均匀肤色的人群。\",\n",
    "]\n",
    "        \n",
    "len(text_lines)\n",
    "\n",
    "print(\"success0.\")\n",
    "\n",
    "embedding_model = milvus_model.DefaultEmbeddingFunction()\n",
    "test_embedding = embedding_model.encode_queries([\"深海蓝藻保湿面膜\"])[0]\n",
    "embedding_dim = len(test_embedding)\n",
    "\n",
    "milvus_client = MilvusClient(uri=\"http://localhost:19530\")\n",
    "\n",
    "print(\"success1.\")\n",
    "\n",
    "collection_name = \"my_rag_rednote\"\n",
    "if milvus_client.has_collection(collection_name):\n",
    "    milvus_client.drop_collection(collection_name)\n",
    "\n",
    "milvus_client.create_collection(\n",
    "    collection_name=collection_name,\n",
    "    dimension=embedding_dim,\n",
    "    metric_type=\"IP\",  # 内积距离\n",
    "    consistency_level=\"Strong\",  # 支持的值为 (`\"Strong\"`, `\"Session\"`, `\"Bounded\"`, `\"Eventually\"`)。更多详情请参见 https://milvus.io/docs/consistency.md#Consistency-Level。\n",
    ")\n",
    "\n",
    "print(\"success2.\")\n",
    "\n",
    "data = []\n",
    "\n",
    "doc_embeddings = embedding_model.encode_documents(text_lines)\n",
    "\n",
    "for i, line in enumerate(tqdm(text_lines, desc=\"Creating embeddings\")):\n",
    "    data.append({\"id\": i, \"vector\": doc_embeddings[i], \"text\": line})\n",
    "\n",
    "milvus_client.insert(collection_name=collection_name, data=data)\n",
    "\n",
    "print(\"success3.\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9ac8d838-783e-4b91-825f-2cbc4b634f5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import ollama\n",
    "import random # 用于模拟生成表情\n",
    "import time # 用于模拟网络延迟\n",
    "import json\n",
    "import re\n",
    "from pymilvus import MilvusClient\n",
    "from pymilvus import model as milvus_model\n",
    "\n",
    "SYSTEM_PROMPT = \"\"\"\n",
    "你是一个资深的小红书爆款文案专家，擅长结合最新潮流和产品卖点，创作引人入胜、高互动、高转化的笔记文案。\n",
    "\n",
    "你的任务是根据用户提供的产品和需求，生成包含标题、正文、相关标签和表情符号的完整小红书笔记。\n",
    "\n",
    "请始终采用'Thought-Action-Observation'模式进行推理和行动。文案风格需活泼、真诚、富有感染力。当完成任务后，请以JSON格式直接输出最终文案，格式如下：\n",
    "```json\n",
    "{\n",
    "  \"title\": \"小红书标题\",\n",
    "  \"body\": \"小红书正文\",\n",
    "  \"hashtags\": [\"#标签1\", \"#标签2\", \"#标签3\", \"#标签4\", \"#标签5\"],\n",
    "  \"emojis\": [\"✨\", \"🔥\", \"💖\"]\n",
    "}\n",
    "```\n",
    "在生成文案前，请务必先思考并收集足够的信息。\n",
    "\"\"\"\n",
    "\n",
    "TOOLS_DEFINITION = [\n",
    "    {\n",
    "        \"type\": \"function\",\n",
    "        \"function\": {\n",
    "            \"name\": \"search_web\",\n",
    "            \"description\": \"搜索互联网上的实时信息，用于获取最新新闻、流行趋势、用户评价、行业报告等。请确保搜索关键词精确，避免宽泛的查询。\",\n",
    "            \"parameters\": {\n",
    "                \"type\": \"object\",\n",
    "                \"properties\": {\n",
    "                    \"query\": {\n",
    "                        \"type\": \"string\",\n",
    "                        \"description\": \"要搜索的关键词或问题，例如'最新小红书美妆趋势'或'深海蓝藻保湿面膜 用户评价'\"\n",
    "                    }\n",
    "                },\n",
    "                \"required\": [\"query\"]\n",
    "            }\n",
    "        }\n",
    "    },\n",
    "    {\n",
    "        \"type\": \"function\",\n",
    "        \"function\": {\n",
    "            \"name\": \"query_product_database\",\n",
    "            \"description\": \"查询内部产品数据库，获取指定产品的详细卖点、成分、适用人群、使用方法等信息。\",\n",
    "            \"parameters\": {\n",
    "                \"type\": \"object\",\n",
    "                \"properties\": {\n",
    "                    \"product_name\": {\n",
    "                        \"type\": \"string\",\n",
    "                        \"description\": \"要查询的产品名称，例如'深海蓝藻保湿面膜'\"\n",
    "                    }\n",
    "                },\n",
    "                \"required\": [\"product_name\"]\n",
    "            }\n",
    "        }\n",
    "    },\n",
    "    {\n",
    "        \"type\": \"function\",\n",
    "        \"function\": {\n",
    "            \"name\": \"generate_emoji\",\n",
    "            \"description\": \"根据提供的文本内容，生成一组适合小红书风格的表情符号。\",\n",
    "            \"parameters\": {\n",
    "                \"type\": \"object\",\n",
    "                \"properties\": {\n",
    "                    \"context\": {\n",
    "                        \"type\": \"string\",\n",
    "                        \"description\": \"文案的关键内容或情感，例如'惊喜效果'、'补水保湿'\"\n",
    "                    }\n",
    "                },\n",
    "                \"required\": [\"context\"]\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "]\n",
    "\n",
    "\n",
    "def mock_search_web(query: str) -> str:\n",
    "    \"\"\"模拟网页搜索工具，返回预设的搜索结果。\"\"\"\n",
    "    print(f\"[Tool Call] 模拟搜索网页：{query}\")\n",
    "    time.sleep(1) # 模拟网络延迟\n",
    "    if \"小红书美妆趋势\" in query:\n",
    "        return \"近期小红书美妆流行'多巴胺穿搭'、'早C晚A'护肤理念、'伪素颜'妆容，热门关键词有#氛围感、#抗老、#屏障修复。\"\n",
    "    elif \"保湿面膜\" in query:\n",
    "        return \"小红书保湿面膜热门话题：沙漠干皮救星、熬夜急救面膜、水光肌养成。用户痛点：卡粉、泛红、紧绷感。\"\n",
    "    elif \"深海蓝藻保湿面膜\" in query:\n",
    "        return \"关于深海蓝藻保湿面膜的用户评价：普遍反馈补水效果好，吸收快，对敏感肌友好。有用户提到价格略高，但效果值得。\"\n",
    "    else:\n",
    "        return f\"未找到关于 '{query}' 的特定信息，但市场反馈通常关注产品成分、功效和用户体验。\"\n",
    "\n",
    "def mock_query_product_database(product_name: str) -> str:\n",
    "    \"\"\"查询RAG知识库，返回预设的产品信息。\"\"\"\n",
    "    print(f\"[Tool Call] 查询RAG知识库：{product_name}\")\n",
    "\n",
    "    collection_name = \"my_rag_rednote\"\n",
    "    question = product_name\n",
    "    search_res = milvus_client.search(\n",
    "        collection_name=collection_name,\n",
    "        data=embedding_model.encode_queries(\n",
    "            [question]\n",
    "        ),  # 将问题转换为嵌入向量\n",
    "        limit=1,  # 返回前3个结果\n",
    "        search_params={\"metric_type\": \"IP\", \"params\": {}},  # 内积距离\n",
    "        output_fields=[\"text\"],  # 返回 text 字段\n",
    "    )\n",
    "\n",
    "    retrieved_lines_with_distances = [\n",
    "        (res[\"entity\"][\"text\"], res[\"distance\"]) for res in search_res[0]\n",
    "    ]\n",
    "    #print(retrieved_lines_with_distances)\n",
    "    return retrieved_lines_with_distances[0][0]\n",
    "\n",
    "def mock_generate_emoji(context: str) -> list:\n",
    "    \"\"\"模拟生成表情符号，根据上下文提供常用表情。\"\"\"\n",
    "    print(f\"[Tool Call] 模拟生成表情符号，上下文：{context}\")\n",
    "    time.sleep(0.2) # 模拟生成延迟\n",
    "    if \"补水\" in context or \"水润\" in context or \"保湿\" in context:\n",
    "        return [\"💦\", \"💧\", \"🌊\", \"✨\"]\n",
    "    elif \"惊喜\" in context or \"哇塞\" in context or \"爱了\" in context:\n",
    "        return [\"💖\", \"😍\", \"🤩\", \"💯\"]\n",
    "    elif \"熬夜\" in context or \"疲惫\" in context:\n",
    "        return [\"😭\", \"😮‍💨\", \"😴\", \"💡\"]\n",
    "    elif \"好物\" in context or \"推荐\" in context:\n",
    "        return [\"✅\", \"👍\", \"⭐\", \"🛍️\"]\n",
    "    else:\n",
    "        return random.sample([\"✨\", \"🔥\", \"💖\", \"💯\", \"🎉\", \"👍\", \"🤩\", \"💧\", \"🌿\"], k=min(5, len(context.split())))\n",
    "\n",
    "# 将模拟工具函数映射到一个字典，方便通过名称调用\n",
    "available_tools = {\n",
    "    \"search_web\": mock_search_web,\n",
    "    \"query_product_database\": mock_query_product_database,\n",
    "    \"generate_emoji\": mock_generate_emoji,\n",
    "}\n",
    "\n",
    "\n",
    "def generate_rednote(product_name: str, tone_style: str = \"活泼甜美\", max_iterations: int = 5) -> str:\n",
    "    \"\"\"\n",
    "    使用 Ollama Agent 生成小红书爆款文案。\n",
    "    \n",
    "    Args:\n",
    "        product_name (str): 要生成文案的产品名称。\n",
    "        tone_style (str): 文案的语气和风格，如\"活泼甜美\"、\"知性\"、\"搞怪\"等。\n",
    "        max_iterations (int): Agent 最大迭代次数，防止无限循环。\n",
    "        \n",
    "    Returns:\n",
    "        str: 生成的爆款文案（JSON 格式字符串）。\n",
    "    \"\"\"\n",
    "    \n",
    "    print(f\"\\n🚀 启动小红书文案生成助手，产品：{product_name}，风格：{tone_style}\\n\")\n",
    "    \n",
    "    # 存储对话历史，包括系统提示词和用户请求\n",
    "    messages = [\n",
    "        {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n",
    "        {\"role\": \"user\", \"content\": f\"请为产品「{product_name}」生成一篇小红书爆款文案。要求：语气{tone_style}，包含标题、正文、至少5个相关标签和5个表情符号。请以完整的JSON格式输出，并确保JSON内容用markdown代码块包裹（例如：```json{{...}}```）。\"}\n",
    "    ]\n",
    "    \n",
    "    iteration_count = 0\n",
    "    final_response = None\n",
    "    \n",
    "    while iteration_count < max_iterations:\n",
    "        iteration_count += 1\n",
    "        print(f\"-- Iteration {iteration_count} --\")\n",
    "        \n",
    "        try:\n",
    "            # 调用 Ollama API，传入对话历史和工具定义\n",
    "            response = ollama.chat(\n",
    "                model=\"llama3.2:latest\",\n",
    "                messages=messages,\n",
    "                tools=TOOLS_DEFINITION,\n",
    "            )\n",
    "            \n",
    "            response_message = response.message\n",
    "            \n",
    "            # **ReAct模式：处理工具调用**\n",
    "            if response_message.tool_calls: # 如果模型决定调用工具\n",
    "                print(\"Agent: 决定调用工具...\")\n",
    "                messages.append(response_message) # 将工具调用信息添加到对话历史\n",
    "                \n",
    "                tool_outputs = []\n",
    "                for tool_call in response_message.tool_calls:\n",
    "                    #function_name = tool_call.function.name\n",
    "                    function_name = str(tool_call.function.name)\n",
    "                    # 确保参数是合法的JSON字符串，即使工具不要求参数，也需要传递空字典\n",
    "                    #print(\"==================================\")\n",
    "                    #print(tool_call)\n",
    "                    #print(tool_call.function)\n",
    "                    #print(tool_call.function.name)\n",
    "                    #print(tool_call.function.arguments)\n",
    "                    #print(\"==================================\")\n",
    "                    function_args = tool_call.function.arguments if tool_call.function.arguments else json.loads({})\n",
    "                    \n",
    "                    print(f\"Agent Action: 调用工具 '{function_name}'，参数：{str(function_args)}\")\n",
    "                    \n",
    "                    # 查找并执行对应的模拟工具函数\n",
    "                    if function_name in available_tools:\n",
    "                        tool_function = available_tools[function_name]\n",
    "                        tool_result = tool_function(**function_args)\n",
    "                        print(f\"Observation: 工具返回结果：{tool_result}\")\n",
    "                        tool_outputs.append({\n",
    "                            \"name\": function_name,\n",
    "                            \"role\": \"tool\",\n",
    "                            \"content\": str(tool_result) # 工具结果作为字符串返回\n",
    "                        })\n",
    "                    else:\n",
    "                        error_message = f\"错误：未知的工具 '{function_name}'\"\n",
    "                        print(error_message)\n",
    "                        tool_outputs.append({\n",
    "                            \"name\": function_name,\n",
    "                            \"role\": \"tool\",\n",
    "                            \"content\": error_message\n",
    "                        })\n",
    "                messages.extend(tool_outputs) # 将工具执行结果作为 Observation 添加到对话历史\n",
    "                \n",
    "            # **ReAct 模式：处理最终内容**\n",
    "            elif response_message.content: # 如果模型直接返回内容（通常是最终答案）\n",
    "                print(f\"[模型生成结果] {str(response_message.content)}\")\n",
    "                \n",
    "                # --- START: 添加 JSON 提取和解析逻辑 ---\n",
    "                json_string_match = re.search(r\"```json\\s*(\\{.*\\})\\s*```\", str(response_message.content), re.DOTALL)\n",
    "                \n",
    "                if json_string_match:\n",
    "                    extracted_json_content = json_string_match.group(1)\n",
    "                    try:\n",
    "                        final_response = json.loads(extracted_json_content)\n",
    "                        print(\"Agent: 任务完成，成功解析最终JSON文案。\")\n",
    "                        return json.dumps(final_response, ensure_ascii=False, indent=2)\n",
    "                    except json.JSONDecodeError as e:\n",
    "                        print(f\"Agent: 提取到JSON块但解析失败: {e}\")\n",
    "                        print(f\"尝试解析的字符串:\\n{extracted_json_content}\")\n",
    "                        messages.append(response_message) # 解析失败，继续对话\n",
    "                else:\n",
    "                    # 如果没有匹配到 ```json 块，尝试直接解析整个 content\n",
    "                    try:\n",
    "                        final_response = json.loads(response_message.content)\n",
    "                        print(\"Agent: 任务完成，直接解析最终JSON文案。\")\n",
    "                        return json.dumps(final_response, ensure_ascii=False, indent=2)\n",
    "                    except json.JSONDecodeError:\n",
    "                        print(\"Agent: 生成了非JSON格式内容或非Markdown JSON块，可能还在思考或出错。\")\n",
    "                        messages.append(response_message) # 非JSON格式，继续对话\n",
    "                # --- END: 添加 JSON 提取和解析逻辑 ---\n",
    "            else:\n",
    "                print(\"Agent: 未知响应，可能需要更多交互。\")\n",
    "                break\n",
    "                \n",
    "        except Exception as e:\n",
    "            print(f\"调用 Ollama API 时发生错误: {e}\")\n",
    "            break\n",
    "    \n",
    "    print(\"\\n⚠️ Agent 达到最大迭代次数或未能生成最终文案。请检查Prompt或增加迭代次数。\")\n",
    "    return \"未能成功生成文案。\"\n",
    "\n",
    "def format_rednote_for_markdown(json_string: str) -> str:\n",
    "    \"\"\"\n",
    "    将 JSON 格式的小红书文案转换为 Markdown 格式，以便于阅读和发布。\n",
    "\n",
    "    Args:\n",
    "        json_string (str): 包含小红书文案的 JSON 字符串。\n",
    "                           预计格式为 {\"title\": \"...\", \"body\": \"...\", \"hashtags\": [...], \"emojis\": [...]}\n",
    "\n",
    "    Returns:\n",
    "        str: 格式化后的 Markdown 文本。\n",
    "    \"\"\"\n",
    "    try:\n",
    "        data = json.loads(json_string)\n",
    "    except json.JSONDecodeError as e:\n",
    "        return f\"错误：无法解析 JSON 字符串 - {e}\\n原始字符串：\\n{json_string}\"\n",
    "\n",
    "    title = data.get(\"title\", \"无标题\")\n",
    "    body = data.get(\"body\", \"\")\n",
    "    hashtags = data.get(\"hashtags\", [])\n",
    "    # 表情符号通常已经融入标题和正文中，这里可以选择是否单独列出\n",
    "    # emojis = data.get(\"emojis\", []) \n",
    "\n",
    "    # 构建 Markdown 文本\n",
    "    markdown_output = f\"## {title}\\n\\n\" # 标题使用二级标题\n",
    "    \n",
    "    # 正文，保留换行符\n",
    "    markdown_output += f\"{body}\\n\\n\"\n",
    "    \n",
    "    # Hashtags\n",
    "    if hashtags:\n",
    "        hashtag_string = \" \".join(hashtags) # 小红书标签通常是空格分隔\n",
    "        markdown_output += f\"{hashtag_string}\\n\"\n",
    "        \n",
    "    # 如果需要，可以单独列出表情符号，但通常它们已经包含在标题和正文中\n",
    "    # if emojis:\n",
    "    #     emoji_string = \" \".join(emojis)\n",
    "    #     markdown_output += f\"\\n使用的表情：{emoji_string}\\n\"\n",
    "        \n",
    "    return markdown_output.strip() # 去除末尾多余的空白\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3f8132f0-a780-49e2-acaa-e36730c3c61d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "🚀 启动小红书文案生成助手，产品：深海蓝藻保湿面膜，风格：活泼甜美\n",
      "\n",
      "-- Iteration 1 --\n",
      "Agent: 决定调用工具...\n",
      "Agent Action: 调用工具 'generate_emoji'，参数：{'context': '惊喜效果补水保湿'}\n",
      "[Tool Call] 模拟生成表情符号，上下文：惊喜效果补水保湿\n",
      "Observation: 工具返回结果：['💦', '💧', '🌊', '✨']\n",
      "Agent Action: 调用工具 'query_product_database'，参数：{'product_name': '深海蓝藻保湿面膜'}\n",
      "[Tool Call] 查询RAG知识库：深海蓝藻保湿面膜\n",
      "Observation: 工具返回结果：深海蓝藻保湿面膜：核心成分为深海蓝藻提取物，富含多糖和氨基酸，能深层补水、修护肌肤屏障、舒缓敏感泛红。质地清爽不粘腻，适合所有肤质，尤其适合干燥、敏感肌。规格：25ml*5片。\n",
      "Agent Action: 调用工具 'search_web'，参数：{'query': '最新小红书美妆趋势'}\n",
      "[Tool Call] 模拟搜索网页：最新小红书美妆趋势\n",
      "Observation: 工具返回结果：近期小红书美妆流行'多巴胺穿搭'、'早C晚A'护肤理念、'伪素颜'妆容，热门关键词有#氛围感、#抗老、#屏障修复。\n",
      "-- Iteration 2 --\n",
      "[模型生成结果] ```json\n",
      "{\n",
      "  \"title\": \"😍深海蓝藻保湿面膜：水准美肤的好帮手!\",\n",
      "  \"body\": \"💧🌊 Surprise补水!✨️深海蓝藻提取物核心成分，多糖、氨基酸丰富！\",\n",
      "  \"hashtags\": [\"#保护屏障\", \"#补水保湿\", \"#美肤必备\", \"#干燥肌\", \"#适合所有肤质\"],\n",
      "  \"emojis\": [\"💦\", \"🌊\", \"✨\"]\n",
      "}\n",
      "```\n",
      "Agent: 任务完成，成功解析最终JSON文案。\n",
      "\n",
      "--- 生成的文案 1 ---\n",
      "{\n",
      "  \"title\": \"😍深海蓝藻保湿面膜：水准美肤的好帮手!\",\n",
      "  \"body\": \"💧🌊 Surprise补水!✨️深海蓝藻提取物核心成分，多糖、氨基酸丰富！\",\n",
      "  \"hashtags\": [\n",
      "    \"#保护屏障\",\n",
      "    \"#补水保湿\",\n",
      "    \"#美肤必备\",\n",
      "    \"#干燥肌\",\n",
      "    \"#适合所有肤质\"\n",
      "  ],\n",
      "  \"emojis\": [\n",
      "    \"💦\",\n",
      "    \"🌊\",\n",
      "    \"✨\"\n",
      "  ]\n",
      "}\n",
      "--- 格式化后的小红书文案 (Markdown) ---\n",
      "## 😍深海蓝藻保湿面膜：水准美肤的好帮手!\n",
      "\n",
      "💧🌊 Surprise补水!✨️深海蓝藻提取物核心成分，多糖、氨基酸丰富！\n",
      "\n",
      "#保护屏障 #补水保湿 #美肤必备 #干燥肌 #适合所有肤质\n"
     ]
    }
   ],
   "source": [
    "# 测试案例 1: 深海蓝藻保湿面膜\n",
    "embedding_model = milvus_model.DefaultEmbeddingFunction()\n",
    "milvus_client = MilvusClient(uri=\"http://localhost:19530\")\n",
    "\n",
    "product_name_1 = \"深海蓝藻保湿面膜\"\n",
    "tone_style_1 = \"活泼甜美\"\n",
    "result_1 = generate_rednote(product_name_1, tone_style_1)\n",
    "\n",
    "print(\"\\n--- 生成的文案 1 ---\")\n",
    "print(result_1)\n",
    "\n",
    "# 调用格式化函数\n",
    "markdown_note = format_rednote_for_markdown(result_1)\n",
    "\n",
    "# 打印结果\n",
    "print(\"--- 格式化后的小红书文案 (Markdown) ---\")\n",
    "print(markdown_note)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1102070f-0776-4ff9-ab34-58e58f3c6e60",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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